{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T02:18:35Z","timestamp":1761877115412,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Foundation for Science and Technology","award":["12096"],"award-info":[{"award-number":["12096"]}]},{"name":"Base Funding","award":["UIDB\/04708\/2020"],"award-info":[{"award-number":["UIDB\/04708\/2020"]}]},{"name":"Programmatic Funding","award":["UIDP\/04708\/2020"],"award-info":[{"award-number":["UIDP\/04708\/2020"]}]},{"name":"CONSTRUCT Research Unit\u2014Institute for R&D in Structures and Construction"},{"DOI":"10.13039\/501100001871","name":"national funds through FCT\/MCTES","doi-asserted-by":"publisher","award":["2022.00898"],"award-info":[{"award-number":["2022.00898"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"FEDER funds through COMPETE2020-Programa Operacional Competitividade e Internacionaliza\u00e7\u00e3o"},{"name":"CEECIND","award":["10.54499\/2022.00898.CEECIND\/CP1733\/CT0005"],"award-info":[{"award-number":["10.54499\/2022.00898.CEECIND\/CP1733\/CT0005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies\u2014Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)\u2014were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering.<\/jats:p>","DOI":"10.3390\/app152111609","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T01:23:59Z","timestamp":1761873839000},"page":"11609","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-Based Machine Learning for Vibration Mitigation by Open Buried Trenches"],"prefix":"10.3390","volume":"15","author":[{"given":"Lu\u00eds","family":"Pereira","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, ISISE, ARISE, University of Coimbra, 3030-788 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2989-375X","authenticated-orcid":false,"given":"Lu\u00eds","family":"Godinho","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, ISISE, ARISE, University of Coimbra, 3030-788 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8648-678X","authenticated-orcid":false,"given":"Fernando G.","family":"Branco","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, ISISE, ARISE, University of Coimbra, 3030-788 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8515-8664","authenticated-orcid":false,"given":"Paulo","family":"da Venda Oliveira","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, ISISE, ARISE, University of Coimbra, 3030-788 Coimbra, Portugal"}]},{"given":"Pedro","family":"Alves Costa","sequence":"additional","affiliation":[{"name":"CONSTRUCT-FEUP, R. Roberto Frias, Universidade do Porto, 4200-464 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2224-8977","authenticated-orcid":false,"given":"Aires","family":"Cola\u00e7o","sequence":"additional","affiliation":[{"name":"CONSTRUCT-FEUP, R. Roberto Frias, Universidade do Porto, 4200-464 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/j.proeng.2017.09.401","article-title":"Mitigation of vibrations and re-radiated noise in buildings generated by railway traffic: A parametric study","volume":"199","author":"Costa","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105822","DOI":"10.1016\/j.soildyn.2019.105822","article-title":"Numerical analysis of buried trench in screening surface vibration","volume":"126","author":"Feng","year":"2019","journal-title":"Soil Dyn. Earthq. 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